{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T20:23:48Z","timestamp":1785011028137,"version":"3.55.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,15]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Fitting parametric models by optimizing frequency-domain objective functions is an attractive approach of parameter estimation in time series analysis. Whittle estimators are a prominent example in this context. Under weak conditions and the assumption that the true spectral density of the underlying process does not necessarily belong to the parametric class of spectral densities fitted, the distribution of Whittle estimators typically depends on difficult to estimate characteristics of the underlying process. This makes the implementation of asymptotic results for the construction of confidence intervals or for assessing the variability of estimators difficult in practice. In this paper we propose a frequency-domain bootstrap method to estimate the distribution of Whittle estimators that is asymptotically valid under assumptions that not only allow for possible model misspecification, but also for weak dependence conditions that are satisfied by a wide range of stationary stochastic processes. Adaptations of the bootstrap procedure developed to incorporate different modifications of Whittle estimators proposed in the literature, such as, for instance, tapered, debiased or boundary extended Whittle estimators, are also considered. Simulations demonstrate the capabilities of the bootstrap method proposed and its good finite sample performance. A real-life data analysis on sunspots is also presented.<\/jats:p>","DOI":"10.1093\/biomet\/asac044","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T19:48:10Z","timestamp":1660074490000},"page":"499-518","source":"Crossref","is-referenced-by-count":7,"title":["Bootstrapping Whittle estimators"],"prefix":"10.1093","volume":"110","author":[{"given":"J -P","family":"Kreiss","sequence":"first","affiliation":[{"name":"Technische Universit\u00e4t Braunschweig, Universit\u00e4tsplatz 2 Institut f\u00fcr Mathematische Stochastik, , D-38106 Braunschweig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E","family":"Paparoditis","sequence":"additional","affiliation":[{"name":"University of Cyprus Department of Mathematics and Statistics, , University Avenue 1, 1678 Nicosia, Cyprus"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"key":"2023051508572739900_B1","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1093\/biomet\/77.4.669","article-title":"Identification of nonlinear time series: first order characterization and order determination","volume":"77","author":"Auestad,","year":"1990","journal-title":"Biometrika"},{"key":"2023051508572739900_B2","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1111\/j.1467-9892.1987.tb00418.x","article-title":"Determining the bandwidth of a kernel spectrum estimate","volume":"8","author":"Beltr\u00e3o,","year":"1987","journal-title":"J. Time Ser. Anal."},{"key":"2023051508572739900_B3","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4419-0320-4","volume-title":"Time Series: Theory and Methods","author":"Brockwell,","year":"1991","edition":"2nd"},{"key":"2023051508572739900_B4","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1214\/aos\/1176350838","article-title":"Small sample effects in time series analysis: a new asymptotic theory and a new estimate","volume":"16","author":"Dahlhaus,","year":"1988","journal-title":"Ann. Statist."},{"key":"2023051508572739900_B5","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1214\/aos\/1069362304","article-title":"A frequency domain bootstrap for ratio statistics in time series analysis","volume":"24","author":"Dahlhaus,","year":"1996","journal-title":"Ann. 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